Dynamic Monte Carlo Simulation Management
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Solution Overview
Problem
Monte Carlo simulations face challenges in determining the optimal number of runs required for statistically accurate analysis, often leading to inefficient use of computational resources and time due to either insufficient or excessive runs, especially when input parameters or relationships change.
Innovation Solution
A computing system dynamically manages Monte Carlo simulations by identifying convergence criteria and adjusting the number of runs, supplementing or reducing the set of outcomes based on statistical parameters and confidence intervals to ensure statistically accurate results while conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the number of deterministic computations is increased to ensure statistically accurate results, then the reliability of the analysis is improved, but the computational time and resource consumption increase
Solution Approach 1:
The patent implements dynamic management of Monte Carlo simulations by automatically adjusting the number of runs based on convergence criteria. The system monitors statistical parameters during execution and adaptively determines when sufficient outcomes have been generated, transforming the static fixed-run approach into a dynamic process that optimizes computational effort while ensuring statistical accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously evaluating convergence criteria based on statistical parameters from generated outcomes. This feedback loop allows the system to monitor whether the simulation has achieved sufficient statistical accuracy and automatically adjust or terminate the simulation accordingly, preventing both insufficient and excessive runs.
2Measurement precision
If the number of deterministic computations is increased to span the input parameter space, then the measurement precision is improved, but the computational resources consumed increase
Solution Approach 1:
The patent applies partial action by generating only the necessary number of outcomes required to meet convergence criteria rather than performing a predetermined excessive number of runs. The system dynamically determines the sufficient sample size needed to achieve statistical accuracy, avoiding unnecessary computational expenditure while ensuring adequate coverage of the input parameter space.
Solution Approach 2:
The system changes parameters dynamically by adjusting the number of simulation runs based on observed convergence behavior. Rather than using a fixed parameter for the number of runs, the system modifies this parameter adaptively based on statistical measurements, allowing optimization of both accuracy and resource utilization.
3Ease of operation
If manual estimation of the number of runs is used, then the ease of operation is maintained, but the productivity decreases due to trial-and-error iterations
Solution Approach 1:
The system implements self-service by automatically managing the simulation execution process. It autonomously determines when to start, continue, or terminate simulations based on convergence criteria without requiring manual intervention or trial-and-error iterations. The system serves itself by monitoring its own performance and making adaptive decisions, thereby improving productivity while maintaining ease of use through automated control.
4Reliability
If the set of outcomes is increased to ensure comprehensive analysis, then the reliability is improved, but the loss of time in processing and analyzing outcomes increases
Solution Approach 1:
The patent applies preliminary action by establishing convergence criteria and statistical thresholds before initiating the simulation. This pre-planned framework allows the system to efficiently process outcomes as they are generated, stopping immediately when criteria are met rather than processing excessive outcomes. The preliminary setup enables streamlined processing that ensures comprehensive analysis without unnecessary time expenditure.
Data Source
AI summary
A computing system, method and computer program product dynamically manage Monte Carlo simulations. In a method, convergence criteria conditions are identified and a set of outcomes is generated following the repeated performance of one or more functions upon randomly sampled input parameter distributions. The method also determines whether the convergence criteria condition is satisfied based upon a plurality of samples. In an instance in which the convergence criteria condition is not satisfied, the method supplements the set of outcomes by repeatedly performing the one or more functions upon randomly sampled input parameter distributions. In an instance in which the convergence criteria condition is satisfied, the method determines whether the set of outcomes has been supplemented prior to satisfying the convergence criteria condition and, if the set of outcomes has not been supplemented, the size of the set of outcomes to be generated during a subsequent Monte Carlo simulation is reduced.